The human brain, economic markets, our immune systems — even the formation of clouds.

These are all examples of complex adaptive systems, formed from multiple interacting components, often nonlinear and dynamic, leading to a collective structure and organisation across multiple levels.

With a truly interdisciplinary approach, encompassing several theoretical frameworks, this master’s programme will provide you with a broad and thorough introduction to the theory of complex adaptive systems and their application to the world around us.

Complex Adaptive Systems:

To understand the dynamics of increasingly complex phenomena where standard simulation methods are inadequate, stochastic algorithms, game theory, adaptive programming, self-similarity, chaos theory and statistical methods are used to describe and increase our understanding of complex systems in nature and society, in the end trying to predict the unpredictable. Examples are gene-regulation networks, the motion of dust particles in turbulent air or the dynamics of financial markets.

One example is fluctuations in share and option prices determining the stability of our economy. Other examples are the dynamics of dust particles in the exhaust of diesel engines, the dynamics of biological or artificial populations, earthquake prediction, and last but not least adaptive learning: the problem of teaching a robot how to respond to unexpected changes in its environment.

Truly interdisciplinary and encompassing several theoretical frameworks, this programme provides you with a broad and thorough introduction to the theory of complex systems and their applications to the world around us. You will gain the knowledge and the tools needed to model and simulate complex systems and learn how to use and build algorithms for analysis, optimization and machine learning.

Topics covered:

The subjects of physics, simulation, modelling, robotics and autonomous systems are fundamental areas in the Complex adaptive systems master's programme. The courses handle topics such as programming, agent-based modelling, network theory, turbulence, genetics, game theory, biophysics, morphogenesis, synchronization, chaotic dynamics, fractals and dynamical stochastic process.

The master's programme in Complex Adaptive Systems is based on a physics perspective with a focus on general principles, but it also provides courses in information theory, computer science and optimisation algorithms, ecology and genetics as well as adaptive systems and robotics. Besides traditional lectures on simulation and the theory of complex systems, the programme is largely based on numerical calculations and simulation projects. Depending on your course selection, you will also be able to do practical work in our robotics lab.​

Career outcomes:

Computer modelling and programming skills, together with expertise in a range of modern algorithms, such as deep machine learning and stochastic optimization, acquired in the programme, open a wide range of possibilities on the job market.

Typical employment is often related to data science or advanced engineering topics. For example, in the field of intelligent control systems, such as the development of autonomous driving.

Previous graduates from this programme often find their jobs at larger technology-intensive companies such as Volvo, Volvo Cars, Ericsson, Saab, AstraZeneca, Scania, etc., or smaller start-ups. Some of our previous students have also chosen to continue towards a PhD in a wide spectrum of academic fields ranging from computer science to physics and biotechnology.